In [1]:
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
In [2]:
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
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nltk.download('all')

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[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
Out[4]:
True
In [5]:
# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_movies = pd.read_csv(path + 'ottmovies.csv')
 
df_movies.head()
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Inception 2010 13+ 8.8 87% Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148.0 movie NaN 1 0 0 0 0
1 2 The Matrix 1999 16+ 8.7 88% Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136.0 movie NaN 1 0 0 0 0
2 3 Avengers: Infinity War 2018 13+ 8.4 85% Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149.0 movie NaN 1 0 0 0 0
3 4 Back to the Future 1985 7+ 8.5 96% Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116.0 movie NaN 1 0 0 0 0
4 5 The Good, the Bad and the Ugly 1966 16+ 8.8 97% Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161.0 movie NaN 1 0 1 0 0
In [6]:
# profile = ProfileReport(df_movies)
# profile
In [7]:
def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()
In [8]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                 8457
IMDb                 328
Rotten Tomatoes    10437
Directors            357
Cast                 648
Genres               234
Country              303
Language             437
Plotline            4958
Runtime              382
Seasons            16923
dtype: int64
**************************************************
Missing vaules %age wise :

ID                   0.000000
Title                0.000000
Year                 0.000000
Age                 49.973409
IMDb                 1.938191
Rotten Tomatoes     61.673462
Directors            2.109555
Cast                 3.829108
Genres               1.382734
Country              1.790463
Language             2.582284
Plotline            29.297406
Runtime              2.257283
Kind                 0.000000
Seasons            100.000000
Netflix              0.000000
Hulu                 0.000000
Prime Video          0.000000
Disney+              0.000000
Type                 0.000000
dtype: float64
**************************************************
Pictorial Representation :
In [9]:
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
 
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
 
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
 
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
 
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
In [10]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :
In [11]:
df_movies.head()
Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix
In [12]:
df_movies.describe()
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.0
mean 8462.000000 2003.211901 0.214915 0.062637 0.727235 0.033150 0.0
std 4885.393638 20.526532 0.410775 0.242315 0.445394 0.179034 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 0.0
25% 4231.500000 2001.000000 0.000000 0.000000 0.000000 0.000000 0.0
50% 8462.000000 2012.000000 0.000000 0.000000 1.000000 0.000000 0.0
75% 12692.500000 2016.000000 0.000000 0.000000 1.000000 0.000000 0.0
max 16923.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 0.0
In [13]:
df_movies.corr()
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.217816 -0.644470 -0.129926 0.469301 0.263530 NaN
Year -0.217816 1.000000 0.256151 0.101337 -0.255578 -0.047258 NaN
Netflix -0.644470 0.256151 1.000000 -0.118032 -0.745141 -0.089649 NaN
Hulu -0.129926 0.101337 -0.118032 1.000000 -0.284654 -0.039693 NaN
Prime Video 0.469301 -0.255578 -0.745141 -0.284654 1.000000 -0.289008 NaN
Disney+ 0.263530 -0.047258 -0.089649 -0.039693 -0.289008 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
In [14]:
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
In [15]:
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
 
# udf_movies
In [16]:
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
In [17]:
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
In [18]:
df_movies_age = df_movies.copy()
In [19]:
df_movies_age.drop(df_movies_age.loc[df_movies_age['Age'] == "NA"].index, inplace = True)
df_movies_age.drop(df_movies_age.loc[df_movies_age['Age'] == "NR"].index, inplace = True)
# df_movies_age = df_movies_age[df_movies_age.Age != "NA"]
df_movies_age['Age'] = df_movies_age['Age'].astype(int)
In [20]:
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_age_movies = df_movies_age.loc[df_movies_age['Netflix'] == 1]
hulu_age_movies = df_movies_age.loc[df_movies_age['Hulu'] == 1]
prime_video_age_movies = df_movies_age.loc[df_movies_age['Prime Video'] == 1]
disney_age_movies = df_movies_age.loc[df_movies_age['Disney+'] == 1]
In [21]:
df_movies_age_group = df_movies_age.copy()
In [22]:
plt.figure(figsize = (10, 10))
corr = df_movies_age.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>
In [23]:
df_age_all_movies = df_movies_age

print('\nMovies with Age Rating are : \n')
df_age_all_movies.head(5)

Movies with Age Rating are :
Out[23]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix
In [24]:
df_age_0_movies = df_movies_age.loc[df_movies_age['Age'] == 0]

print('\nMovies with All Age Rating are : \n')
df_age_0_movies.head(5)

Movies with All Age Rating are :
Out[24]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
54 55 Willy Wonka & the Chocolate Factory 1971 0 7.8 90 Mel Stuart Gene Wilder,Jack Albertson,Peter Ostrum,Roy Ki... Family,Fantasy,Musical United States,United Kingdom English,French,German,Italian The world is astounded when Willy Wonka, for y... 100 movie 1 0 0 0 0 Netflix
121 122 The Princess and the Frog 2009 0 7.1 85 Ron Clements,John Musker Anika Noni Rose,Bruno Campos,Keith David,Micha... Animation,Adventure,Comedy,Family,Fantasy,Musi... United States English,French A modern day retelling of the classic story Th... 97 movie 1 0 0 1 0 Netflix
129 130 Barfi! 2012 0 8.1 86 Anurag Basu Ranbir Kapoor,Priyanka Chopra,Ileana D'Cruz,Sa... Comedy,Drama,Romance India Hindi Set in the 1970s in a pretty corner of India, ... 151 movie 1 0 0 0 0 Netflix
147 148 Swades 2004 0 8.2 83 Ashutosh Gowariker Shah Rukh Khan,Gayatri Joshi,Kishori Ballal,Sm... Drama India Hindi,English Set in modern day India, Swades is a film that... 210 movie 1 0 0 0 0 Netflix
170 171 Kabhi Khushi Kabhie Gham 2001 0 7.4 100 Karan Johar Amitabh Bachchan,Jaya Bachchan,Shah Rukh Khan,... Drama,Musical,Romance India Hindi,English Yashvardhan Raichand lives a very wealthy life... 210 movie 1 0 0 0 0 Netflix
In [25]:
df_age_7_movies = df_movies_age.loc[df_movies_age['Age'] == 7]

print('\nMovies with 7+ Age Rating are : \n')
df_age_7_movies.head(5)

Movies with 7+ Age Rating are :
Out[25]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix
5 6 Spider-Man: Into the Spider-Verse 2018 7 8.4 97 Bob Persichetti,Peter Ramsey,Rodney Rothman Shameik Moore,Jake Johnson,Hailee Steinfeld,Ma... Animation,Action,Adventure,Family,Sci-Fi United States English,Spanish Phil Lord and Christopher Miller, the creative... 117 movie 1 0 0 0 0 Netflix
8 9 Raiders of the Lost Ark 1981 7 8.4 95 Steven Spielberg Harrison Ford,Karen Allen,Paul Freeman,Ronald ... Action,Adventure United States English,German,Hebrew,Spanish,Arabic,Nepali The year is 1936. An archeology professor name... 115 movie 1 0 0 0 0 Netflix
14 15 Monty Python and the Holy Grail 1975 7 8.2 97 Terry Gilliam,Terry Jones Graham Chapman,John Cleese,Eric Idle,Terry Gil... Adventure,Comedy,Fantasy United Kingdom English,French,Latin History is turned on its comic head when, in t... 91 movie 1 0 0 0 0 Netflix
17 18 Groundhog Day 1993 7 8 96 Harold Ramis Bill Murray,Andie MacDowell,Chris Elliott,Step... Comedy,Fantasy,Romance United States English,French,Italian A weather man is reluctantly sent to cover a s... 101 movie 1 0 0 0 0 Netflix
In [26]:
df_age_13_movies = df_movies_age.loc[df_movies_age['Age'] == 13]

print('\nMovies with 13+ Age Rating are : \n')
df_age_13_movies.head(5)

Movies with 13+ Age Rating are :
Out[26]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix
11 12 3 Idiots 2009 13 8.4 100 Rajkumar Hirani Aamir Khan,Madhavan,Sharman Joshi,Kareena Kapo... Comedy,Drama India Hindi,English Farhan Qureshi and Raju Rastogi want to re-uni... 170 movie 1 0 1 0 0 Netflix
15 16 Once Upon a Time in the West 1968 13 8.5 95 Sergio Leone Claudia Cardinale,Henry Fonda,Jason Robards,Ch... Western Italy,United States English,Italian,Spanish Jill McBain travels to the wild frontier; Utah... 165 movie 1 0 1 0 0 Netflix
16 17 Indiana Jones and the Last Crusade 1989 13 8.2 88 Steven Spielberg Harrison Ford,Sean Connery,Denholm Elliott,Ali... Action,Adventure United States,United Kingdom English,German,Greek,Arabic An art collector appeals to Indiana Jones to e... 127 movie 1 0 0 0 0 Netflix
In [27]:
df_age_16_movies = df_movies_age.loc[df_movies_age['Age'] == 16]

print('\nMovies with 16+ Age Rating are : \n')
df_age_16_movies.head(5)

Movies with 16+ Age Rating are :
Out[27]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix
6 7 The Pianist 2002 16 8.5 95 Roman Polanski Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... Biography,Drama,Music,War United Kingdom,France,Poland,Germany,United St... English,German,Russian In this adaptation of the autobiography "The P... 150 movie 1 0 1 0 0 Netflix
7 8 Django Unchained 2012 16 8.4 87 Quentin Tarantino Jamie Foxx,Christoph Waltz,Leonardo DiCaprio,K... Drama,Western United States English,German,French,Italian In 1858, a bounty-hunter named King Schultz se... 165 movie 1 0 0 0 0 Netflix
9 10 Inglourious Basterds 2009 16 8.3 89 Quentin Tarantino Brad Pitt,Mélanie Laurent,Christoph Waltz,Eli ... Adventure,Drama,War United States,Germany English,German,French,Italian In German-occupied France, young Jewish refuge... 153 movie 1 0 0 0 0 Netflix
In [28]:
df_age_18_movies = df_movies_age.loc[df_movies_age['Age'] == 18]

print('\nMovies with 18+ Age Rating are : \n')
df_age_18_movies.head(5)

Movies with 18+ Age Rating are :
Out[28]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
67 68 El Camino: A Breaking Bad Movie 2019 18 7.3 91 Vince Gilligan Aaron Paul,Jonathan Banks,Matt Jones,Charles B... Action,Crime,Drama United States English,Spanish Finally free from torture and slavery at the h... 122 movie 1 0 0 0 0 Netflix
71 72 The Evil Dead 1981 18 7.5 63 Sam Raimi Bruce Campbell,Ellen Sandweiss,Richard DeManin... Horror United States English Five college students take time off to spend a... 85 movie 1 0 0 0 0 Netflix
74 75 Blue Is the Warmest Color 2013 18 7.7 89 Abdellatif Kechiche Léa Seydoux,Adèle Exarchopoulos,Salim Kechiouc... Drama,Romance France,Belgium,Spain French,English Adèle is a high school student who is beginnin... 180 movie 1 0 0 0 0 Netflix
85 86 13th 2016 18 8.3 97 Ava DuVernay Melina Abdullah,Michelle Alexander,Cory Booker... Documentary,Crime,History United States English The film begins with the idea that 25 percent ... 100 movie 1 0 0 0 0 Netflix
90 91 The Platform 2019 18 7 80 Galder Gaztelu-Urrutia Ivan Massagué,Zorion Eguileor,Antonia San Juan... Horror,Sci-Fi,Thriller Spain Spanish,Italian A mysterious place, an indescribable prison, a... 94 movie 1 0 0 0 0 Netflix
In [29]:
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_age['Age'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_age['Age'], ax = ax[1])
plt.show()
In [30]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Age s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_age_movies['Age'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_age_movies['Age'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_age_movies['Age'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_age_movies['Age'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
In [31]:
def round_val(data):
    if str(data) != 'nan':
        return round(data)
In [32]:
df_movies_age_group['Age Group'] = df_movies_age['Age'].apply(round_val)
 
age_values = df_movies_age_group['Age Group'].value_counts().sort_index(ascending = False).tolist()
age_index = df_movies_age_group['Age Group'].value_counts().sort_index(ascending = False).index
 
# age_values, age_index
In [33]:
age_group_count = df_movies_age_group.groupby('Age Group')['Title'].count()
age_group_movies = df_movies_age_group.groupby('Age Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
age_group_data_movies = pd.concat([age_group_count, age_group_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
age_group_data_movies = age_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
In [34]:
# Age Group with Movies Counts - All Platforms Combined
age_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
Out[34]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
3 16 3316 635 332 2509 12
1 7 1687 353 150 1067 195
2 13 1612 430 147 1068 93
4 18 991 327 56 653 2
0 0 907 132 52 489 259
In [35]:
age_group_data_movies.sort_values(by = 'Age Group', ascending = False)
Out[35]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
4 18 991 327 56 653 2
3 16 3316 635 332 2509 12
2 13 1612 430 147 1068 93
1 7 1687 353 150 1067 195
0 0 907 132 52 489 259
In [36]:
fig = px.bar(y = age_group_data_movies['Movies Count'],
             x = age_group_data_movies['Age Group'], 
             color = age_group_data_movies['Age Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies Count', 'x' : 'Age : '},
             title  = 'Movies with Group Age : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()
In [37]:
fig = px.pie(age_group_data_movies,
             names = age_group_data_movies['Age Group'],
             values = age_group_data_movies['Movies Count'],
             color = age_group_data_movies['Movies Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'Movies Count based on Age Group')
fig.show()
In [38]:
df_age_group_high_movies = age_group_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_age_group_high_movies = df_age_group_high_movies.drop(['index'], axis = 1)
# filter = (age_group_data_movies['Movies Count'] ==  (age_group_data_movies['Movies Count'].max()))
# df_age_group_high_movies = age_group_data_movies[filter]
 
# highest_rated_movies = age_group_data_movies.loc[age_group_data_movies['Movies Count'].idxmax()]
 
# print('\nAge with Highest Ever Movies Count are : All Platforms Combined\n')
df_age_group_high_movies.head(5)
Out[38]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 16 3316 635 332 2509 12
1 7 1687 353 150 1067 195
2 13 1612 430 147 1068 93
3 18 991 327 56 653 2
4 0 907 132 52 489 259
In [39]:
df_age_group_low_movies = age_group_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_age_group_low_movies = df_age_group_low_movies.drop(['index'], axis = 1)
# filter = (age_group_data_movies['Movies Count'] = =  (age_group_data_movies['Movies Count'].min()))
# df_age_group_low_movies = age_group_data_movies[filter]
 
# print('\nAge with Lowest Ever Movies Count are : All Platforms Combined\n')
df_age_group_low_movies.head(5)
Out[39]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 0 907 132 52 489 259
1 18 991 327 56 653 2
2 13 1612 430 147 1068 93
3 7 1687 353 150 1067 195
4 16 3316 635 332 2509 12
In [40]:
print(f'''
      Total '{df_movies_age['Age'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see Movies from Total '{age_group_data_movies['Age Group'].unique().shape[0]}' Age Group, They were Like this, \n
 
      {age_group_data_movies.sort_values(by = 'Movies Count', ascending = False)['Age Group'].unique()} etc. \n
 
      The Age Group with Highest Movies Count have '{age_group_data_movies['Movies Count'].max()}' Movies Available is '{df_age_group_high_movies['Age Group'][0]}', &\n
      The Age Group with Lowest Movies Count have '{age_group_data_movies['Movies Count'].min()}' Movies Available is '{df_age_group_low_movies['Age Group'][0]}'
      ''')

      Total '8513' Titles are available on All Platforms, out of which

      You Can Choose to see Movies from Total '5' Age Group, They were Like this, 

 
      [16  7 13 18  0] etc. 

 
      The Age Group with Highest Movies Count have '3316' Movies Available is '16', &

      The Age Group with Lowest Movies Count have '907' Movies Available is '0'
In [41]:
netflix_age_group_movies = age_group_data_movies[age_group_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_movies = netflix_age_group_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_age_group_high_movies = df_age_group_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_high_movies = netflix_age_group_high_movies.drop(['index'], axis = 1)
 
netflix_age_group_low_movies = df_age_group_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_age_group_low_movies = netflix_age_group_low_movies.drop(['index'], axis = 1)
 
netflix_age_group_high_movies.head(5)
Out[41]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 16 3316 635 332 2509 12
1 13 1612 430 147 1068 93
2 7 1687 353 150 1067 195
3 18 991 327 56 653 2
4 0 907 132 52 489 259
In [42]:
hulu_age_group_movies = age_group_data_movies[age_group_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_movies = hulu_age_group_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_age_group_high_movies = df_age_group_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_high_movies = hulu_age_group_high_movies.drop(['index'], axis = 1)
 
hulu_age_group_low_movies = df_age_group_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_age_group_low_movies = hulu_age_group_low_movies.drop(['index'], axis = 1)
 
hulu_age_group_high_movies.head(5)
Out[42]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 16 3316 635 332 2509 12
1 7 1687 353 150 1067 195
2 13 1612 430 147 1068 93
3 18 991 327 56 653 2
4 0 907 132 52 489 259
In [43]:
prime_video_age_group_movies = age_group_data_movies[age_group_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_movies = prime_video_age_group_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_age_group_high_movies = df_age_group_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_high_movies = prime_video_age_group_high_movies.drop(['index'], axis = 1)
 
prime_video_age_group_low_movies = df_age_group_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_age_group_low_movies = prime_video_age_group_low_movies.drop(['index'], axis = 1)
 
prime_video_age_group_high_movies.head(5)
Out[43]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 16 3316 635 332 2509 12
1 13 1612 430 147 1068 93
2 7 1687 353 150 1067 195
3 18 991 327 56 653 2
4 0 907 132 52 489 259
In [44]:
disney_age_group_movies = age_group_data_movies[age_group_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_movies = disney_age_group_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_age_group_high_movies = df_age_group_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_high_movies = disney_age_group_high_movies.drop(['index'], axis = 1)
 
disney_age_group_low_movies = df_age_group_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_age_group_low_movies = disney_age_group_low_movies.drop(['index'], axis = 1)
 
disney_age_group_high_movies.head(5)
Out[44]:
Age Group Movies Count Netflix Hulu Prime Video Disney+
0 0 907 132 52 489 259
1 7 1687 353 150 1067 195
2 13 1612 430 147 1068 93
3 16 3316 635 332 2509 12
4 18 991 327 56 653 2
In [45]:
print(f'''
      The Age Group with Highest Movies Count Ever Got is '{df_age_group_high_movies['Age Group'][0]}' : '{df_age_group_high_movies['Movies Count'].max()}'\n
      The Age Group with Lowest Movies Count Ever Got is '{df_age_group_low_movies['Age Group'][0]}' : '{df_age_group_low_movies['Movies Count'].min()}'\n
      
      The Age Group with Highest Movies Count on 'Netflix' is '{netflix_age_group_high_movies['Age Group'][0]}' : '{netflix_age_group_high_movies['Netflix'].max()}'\n
      The Age Group with Lowest Movies Count on 'Netflix' is '{netflix_age_group_low_movies['Age Group'][0]}' : '{netflix_age_group_low_movies['Netflix'].min()}'\n
      
      The Age Group with Highest Movies Count on 'Hulu' is '{hulu_age_group_high_movies['Age Group'][0]}' : '{hulu_age_group_high_movies['Hulu'].max()}'\n
      The Age Group with Lowest Movies Count on 'Hulu' is '{hulu_age_group_low_movies['Age Group'][0]}' : '{hulu_age_group_low_movies['Hulu'].min()}'\n
      
      The Age Group with Highest Movies Count on 'Prime Video' is '{prime_video_age_group_high_movies['Age Group'][0]}' : '{prime_video_age_group_high_movies['Prime Video'].max()}'\n
      The Age Group with Lowest Movies Count on 'Prime Video' is '{prime_video_age_group_low_movies['Age Group'][0]}' : '{prime_video_age_group_low_movies['Prime Video'].min()}'\n
      
      The Age Group with Highest Movies Count on 'Disney+' is '{disney_age_group_high_movies['Age Group'][0]}' : '{disney_age_group_high_movies['Disney+'].max()}'\n
      The Age Group with Lowest Movies Count on 'Disney+' is '{disney_age_group_low_movies['Age Group'][0]}' : '{disney_age_group_low_movies['Disney+'].min()}'\n 
      ''')

      The Age Group with Highest Movies Count Ever Got is '16' : '3316'

      The Age Group with Lowest Movies Count Ever Got is '0' : '907'

      
      The Age Group with Highest Movies Count on 'Netflix' is '16' : '635'

      The Age Group with Lowest Movies Count on 'Netflix' is '0' : '132'

      
      The Age Group with Highest Movies Count on 'Hulu' is '16' : '332'

      The Age Group with Lowest Movies Count on 'Hulu' is '0' : '52'

      
      The Age Group with Highest Movies Count on 'Prime Video' is '16' : '2509'

      The Age Group with Lowest Movies Count on 'Prime Video' is '0' : '489'

      
      The Age Group with Highest Movies Count on 'Disney+' is '0' : '259'

      The Age Group with Lowest Movies Count on 'Disney+' is '18' : '2'
In [46]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.barplot(x = netflix_age_group_movies['Age Group'], y = netflix_age_group_movies['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = hulu_age_group_movies['Age Group'], y = hulu_age_group_movies['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = prime_video_age_group_movies['Age Group'], y = prime_video_age_group_movies['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = disney_age_group_movies['Age Group'], y = disney_age_group_movies['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
 
plt.show()
In [47]:
plt.figure(figsize = (20, 5))
sns.lineplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Netflix'], color = 'red')
sns.lineplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Hulu'], color = 'lightgreen')
sns.lineplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Prime Video'], color = 'lightblue')
sns.lineplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Disney+'], color = 'darkblue')
plt.xlabel('Age Group', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
In [48]:
print(f'''
      Accross All Platforms Total Count of Age Group is '{age_group_data_movies['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Netflix' is '{netflix_age_group_movies['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Hulu' is '{hulu_age_group_movies['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Prime Video' is '{prime_video_age_group_movies['Age Group'].unique().shape[0]}'\n
      Total Count of Age Group on 'Disney+' is '{disney_age_group_movies['Age Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Age Group is '5'

      Total Count of Age Group on 'Netflix' is '5'

      Total Count of Age Group on 'Hulu' is '5'

      Total Count of Age Group on 'Prime Video' is '5'

      Total Count of Age Group on 'Disney+' is '5'
In [49]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.lineplot(y = age_group_data_movies['Age Group'], x = age_group_data_movies['Netflix'], color = 'red', ax = axes[0, 0])
h_a_ax2 = sns.lineplot(y = age_group_data_movies['Age Group'], x = age_group_data_movies['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_a_ax3 = sns.lineplot(y = age_group_data_movies['Age Group'], x = age_group_data_movies['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_a_ax4 = sns.lineplot(y = age_group_data_movies['Age Group'], x = age_group_data_movies['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])

plt.show()
In [50]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_a_ax1 = sns.barplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = age_group_data_movies['Age Group'], y = age_group_data_movies['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
 
plt.show()